From Maps to Models


Book Description

The United States faces numerous, varied, and evolving threats to national security, including terrorism, scarcity and disruption of food and water supplies, extreme weather events, and regional conflicts around the world. Effectively managing these threats requires intelligence that not only assesses what is happening now, but that also anticipates potential future threats. The National Geospatial-Intelligence Agency (NGA) is responsible for providing geospatial intelligence on other countriesâ€"assessing where exactly something is, what it is, and why it is importantâ€"in support of national security, disaster response, and humanitarian assistance. NGA's approach today relies heavily on imagery analysis and mapping, which provide an assessment of current and past conditions. However, augmenting that approach with a strong modeling capability would enable NGA to also anticipate and explore future outcomes. A model is a simplified representation of a real-world system that is used to extract explainable insights about the system, predict future outcomes, or explore what might happen under plausible what-if scenarios. Such models use data and/or theory to specify inputs (e.g., initial conditions, boundary conditions, and model parameters) to produce an output. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities describes the types of models and analytical methods used to understand real-world systems, discusses what would be required to make these models and methods useful for geospatial intelligence, and identifies supporting research and development for NGA. This report provides examples of models that have been used to help answer the sorts of questions NGA might ask, describes how to go about a model-based investigation, and discusses models and methods that are relevant to NGA's mission.







MOD Relational Maps Models and MOD Natural Neutrosophic Relational Maps Models


Book Description

In this book the authors for the first time construct MOD Relational Maps model analogous to Fuzzy Relational Maps (FRMs) model or Neutrosophic Relational Maps (NRMs) model using the MOD rectangular or relational matrix. The advantage of using these models is that the MOD fixed point pair or MOD limit cycle pair is obtained after a finite number of iterations.




Modeling Our World


Book Description

Geographic data models are digital frameworks that describe the location and characteristics of things in the world around us. With a geographic information system, we can use these models as lenses to see, interpret, and analyze the infinite complexity of our natural and man-made environments. With the geodatabase, a new geographic data model introduced with ArcInfo 8, you can extend significantly the level of detail and range of accuracy with which you can model geographic reality in a database environment.




The 5th Experimental Chaos Conference


Book Description

The 5th Experimental Chaos Conference was a gathering of scientists and engineers who work on real-world systems that behave in a nonlinear and, often, chaotic fashion. The proceedings present discoveries of chaotic behavior, explanation of nonlinear phenomena in the laboratory, and applications of nonlinear and chaotic effects to devices and techniques for improving performance and surmounting technical obstacles. Experimental work is presented on chaos in semiconductor superlattices, spatiotemporal chaos in magnetic materials, instabilities in magnetic fluids, bifurcations of hexagonal patterns in lasers, and discrete rotating waves. New phenomena are exhibited on amplitude death in coupled oscillators, vortex crystals, wakes in soap films, chaotic dynamics of ocean waves, and microscopic chaos. Applications of chaotic dynamics are offered in the areas of chaotic pulse trains in digital communications, detection of changes in EEGs, detection of unstable periodic orbits in noisy data, cellular automata and warfare, detection of n:m phase synchronization, methods in acoustic chaos, chaos in the machine tool-cutting process, and a nonlinear airfoil. The broad range of topics and fields touches on a wide variety of systems whose behavior is now better understood and applied through the use of chaotic dynamics.




Induced Plithogenic Cognitive Maps with Combined Connection Matrix to investigate the glitches of online learning system


Book Description

Plithogenic Cognitive Maps (PCM) introduced by Nivetha and Smarandache are extensively applied in decision making. This research work extends PCM to Induced PCM by introducing the concept of combined connection matrix (CCM). The proposed induced PCM decision making model with CCM is applied to examine the glitches of online learning system. It was observed that expert’s opinion on the associational impact between the factors considered for study in the form combined connection matrix is more advantageous on comparison with conventional connection matrix representation, as CCM is a mixture of crisp/fuzzy/intuitionistic/neutrosophic representations. The proposed model will certainly facilitate the decision makers in designing optimal solutions to the real time problems and it shall be extended based on the needs of the decision makers and employed in various other decision making environment. The shortcomings of the model are also discussed in brief.




Models of Neural Networks IV


Book Description

This volume, with chapters by leading researchers in the field, is devoted to early vision and attention, that is, to the first stages of visual information processing. This state-of-the-art look at biological neural networks spans the many subfields, such as computational and experimental neuroscience; anatomy and physiology; visual information processing and scene segmentation; perception at illusory contours; control of visual attention; and paradigms for computing with spiking neurons.




Computational Maps in the Visual Cortex


Book Description

For more than 30 years, the visual cortex has been the source of new theories and ideas about how the brain processes information. The visual cortex is easily accessible through a variety of recording and imagining techniques and allows mapping of high level behavior relatively directly to neural mechanisms. Understanding the computations in the visual cortex is therefore an important step toward a general theory of computational brain theory.







Self-organizing Map Formation


Book Description

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang